Transfer physics informed neural network: a new framework for distributed physics informed neural networks via parameter sharing
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Transfer physics informed neural network: a new framework for distributed physics informed neural networks via parameter sharing
Authors
Keywords
-
Journal
ENGINEERING WITH COMPUTERS
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-07-20
DOI
10.1007/s00366-022-01703-9
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Solving inverse heat transfer problems without surrogate models - a fast, data-sparse, Physics Informed Neural Network approach
- (2022) Vivek Oommen et al. JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING
- Physics informed neural networks for continuum micromechanics
- (2022) Alexander Henkes et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- A framework based on physics-informed neural networks and extreme learning for the analysis of composite structures
- (2022) C.A. Yan et al. COMPUTERS & STRUCTURES
- Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks
- (2021) Qiming Zhu et al. COMPUTATIONAL MECHANICS
- A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications
- (2021) Navid Zobeiry et al. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
- Neural network method: delay and system of delay differential equations
- (2021) Shagun Panghal et al. ENGINEERING WITH COMPUTERS
- Physics-Informed Neural Networks for Heat Transfer Problems
- (2021) Shengze Cai et al. JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME
- Physics informed neural networks for simulating radiative transfer
- (2021) Siddhartha Mishra et al. JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER
- A physics-informed deep learning method for solving direct and inverse heat conduction problems of materials
- (2021) Zhili He et al. Materials Today Communications
- Physics-informed neural network for modelling the thermochemical curing process of composite-tool systems during manufacture
- (2021) Sina Amini Niaki et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Parametric deep energy approach for elasticity accounting for strain gradient effects
- (2021) Vien Minh Nguyen-Thanh et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations
- (2021) Suchuan Dong et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs
- (2021) Amuthan A. Ramabathiran et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Optimization free neural network approach for solving ordinary and partial differential equations
- (2020) Shagun Panghal et al. ENGINEERING WITH COMPUTERS
- An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
- (2020) E. Samaniego et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
- (2020) Ameya D. Jagtap et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
- (2020) Ameya D. Jagtap et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
- (2020) Ehsan Haghighat et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Distributed learning machines for solving forward and inverse problems in partial differential equations
- (2020) Vikas Dwivedi et al. NEUROCOMPUTING
- hp-VPINNs: Variational physics-informed neural networks with domain decomposition
- (2020) Ehsan Kharazmi et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
- (2020) Han Gao et al. JOURNAL OF COMPUTATIONAL PHYSICS
- NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
- (2020) Xiaowei Jin et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Bilinear neural network method to obtain the exact analytical solutions of nonlinear partial differential equations and its application to p-gBKP equation
- (2019) Run-Fa Zhang et al. NONLINEAR DYNAMICS
- Prediction of ultimate bearing capacity through various novel evolutionary and neural network models
- (2019) Hossein Moayedi et al. ENGINEERING WITH COMPUTERS
- A deep learning solution approach for high-dimensional random differential equations
- (2019) Mohammad Amin Nabian et al. PROBABILISTIC ENGINEERING MECHANICS
- Physics Informed Extreme Learning Machine (PIELM)–A rapid method for the numerical solution of partial differential equations
- (2019) Vikas Dwivedi et al. NEUROCOMPUTING
- Transfer learning enhanced physics informed neural network for phase-field modeling of fracture
- (2019) Somdatta Goswami et al. THEORETICAL AND APPLIED FRACTURE MECHANICS
- Prediction of building damage induced by tunnelling through an optimized artificial neural network
- (2018) S. Moosazadeh et al. ENGINEERING WITH COMPUTERS
- A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls
- (2018) Ebrahim Noroozi Ghaleini et al. ENGINEERING WITH COMPUTERS
- Estimating and optimizing safety factors of retaining wall through neural network and bee colony techniques
- (2018) Behrouz Gordan et al. ENGINEERING WITH COMPUTERS
- DGM: A deep learning algorithm for solving partial differential equations
- (2018) Justin Sirignano et al. JOURNAL OF COMPUTATIONAL PHYSICS
- A unified deep artificial neural network approach to partial differential equations in complex geometries
- (2018) Jens Berg et al. NEUROCOMPUTING
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- (2018) M. Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Approximate solutions by artificial neural network of hybrid fuzzy differential equations
- (2017) Mahmoud Paripour et al. Advances in Mechanical Engineering
- Artificial neural network approach for a class of fractional ordinary differential equation
- (2016) Ahmad Jafarian et al. NEURAL COMPUTING & APPLICATIONS
- A constrained integration (CINT) approach to solving partial differential equations using artificial neural networks
- (2015) Keith Rudd et al. NEUROCOMPUTING
- Heterogeneous Heat Conduction Problems by an Improved Element-Free Galerkin Method
- (2014) Xiaohua Zhang et al. NUMERICAL HEAT TRANSFER PART B-FUNDAMENTALS
- Artificial neural networks in medical diagnosis
- (2013) Filippo Amato et al. Journal of Applied Biomedicine
- The lid-driven square cavity flow: numerical solution with a 1024 x 1024 grid
- (2010) Carlos Henrique Marchi et al. Journal of the Brazilian Society of Mechanical Sciences and Engineering
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExplorePublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More